Abstract
Big data analytics uncovers hidden patterns, correlations and other insights by examining large amounts of data. Deep Learning can play a role in developing solutions from large datasets, and is an important tool in the big data analytics toolbox. Deep Learning has been recently employed to solve various problems in computer vision and demonstrated state-of-the-art performance on visual recognition tasks. In medical imaging, especially in brain tumor cancer diagnosis and treatment plan development, accurate and reliable brain tumor segmentation plays a critical role. In this chapter, we describe brain tumor segmentation using Deep Learning. We constructed a 6-layer Dense Convolutional Network, which connects each layer to every subsequent layer in a feed-forward fashion. This specific connectivity architecture ensures the maximum information flow between layers in the network and strengthens the feature propagation from layer to layer. We show how this arrangement increases the efficiency during training and the accuracy of the results. We have trained and evaluated our method based on the imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2017. The described method is able to segment the whole tumor (WT) region of the high-grade brain tumor gliomas using T1 Magnetic Resonance Images (MRI) and with excellent segmentation results.
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Notes
- 1.
Definition of “overfitting” at OxfordDictionaries.com: this definition is specifically for Statistics.
References
Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1959)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS’12 Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105 (2012)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Çiçek, Ö., et al.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention Lecture Notes in Computer Science, vol. 9901. Springer, Cham (2016)
Huang, G., et al.: Densely connected convolutional networks. arXiv preprint arXiv:1608.06993 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)
Goetz, M., et al.: DALSA: domain adaptation for supervised learning from sparsely annotated MR images. IEEE Trans. Med. Imaging. 35(1), 184–196 (2016)
Dvorak, P., Menze, B.H.: Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation. In: Menze, B., et al. (eds.) Medical Computer Vision: Algorithms for Big Data. MCV 2015 Lecture Notes in Computer Science, vol. 9601. Springer, Cham (2016)
Pereira, S., et al.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer, Cham (2015)
Darvin, Y., et al.: 3-D convolutional neural networks for glioblastoma segmentation. arXiv preprint arXiv:1611.04534 (2016)
MICCAI: BraTS 2017 dataset (July 25, 2017). http://braintumorsegmentation.org/ (2017)
MICCAI: BraTS 2012 dataset (July 25, 2017). http://www2.imm.dtu.dk/projects/BRATS2012/ (2017)
MICCAI: BraTS 2013 dataset (July 25, 2017). http://martinos.org/qtim/miccai2013/ (2017)
Havaei, M., et al.: A convolutional neural network approach to brain tumor segmentation. In: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer, Cham (2015)
Shin, H.-C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging. 35(5), 1285–1298 (2016)
Keras: The Python Deep Learning Library: (July 25, 2017): https://keras.io/ (2017)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology. 26(3), 297–302 (1945)
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Zhang, Z., Odaibo, D., Skidmore, F.M., Tanik, M.M. (2017). A Big Data Analytics Approach in Medical Imaging Segmentation Using Deep Convolutional Neural Networks. In: Suh, S., Anthony, T. (eds) Big Data and Visual Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-63917-8_10
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DOI: https://doi.org/10.1007/978-3-319-63917-8_10
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